class_docstr: Anchor-free head used in `NASFCOS <https://arxiv.org/abs/1906.04423>`_.
  
  It is quite similar with FCOS head, except for the searched structure of
  classification branch and bbox regression branch, where a structure of
  "dconv3x3, conv3x3, dconv3x3, conv1x1" is utilized instead.
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      strides (Sequence[int] or Sequence[Tuple[int, int]]): Strides of points
          in multiple feature levels. Defaults to (4, 8, 16, 32, 64).
      regress_ranges (Sequence[Tuple[int, int]]): Regress range of multiple
          level points.
      center_sampling (bool): If true, use center sampling.
          Defaults to False.
      center_sample_radius (float): Radius of center sampling.
          Defaults to 1.5.
      norm_on_bbox (bool): If true, normalize the regression targets with
          FPN strides. Defaults to False.
      centerness_on_reg (bool): If true, position centerness on the
          regress branch. Please refer to https://github.com/tianzhi0549/FCOS/issues/89#issuecomment-516877042.
          Defaults to False.
      conv_bias (bool or str): If specified as `auto`, it will be decided by
          the norm_cfg. Bias of conv will be set as True if `norm_cfg` is
          None, otherwise False. Defaults to "auto".
      loss_cls (:obj:`ConfigDict` or dict): Config of classification loss.
      loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss.
      loss_centerness (:obj:`ConfigDict`, or dict): Config of centerness
          loss.
      norm_cfg (:obj:`ConfigDict` or dict): dictionary to construct and
          config norm layer.  Defaults to
          ``norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)``.
      init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or             dict], opitonal): Initialization config dict.
code: def __init__(
      self, *args, init_cfg: OptMultiConfig = None, **kwargs
  ) -> None:
      if init_cfg is None:
          init_cfg = [
              dict(type="Caffe2Xavier", layer=["ConvModule", "Conv2d"]),
              dict(
                  type="Normal",
                  std=0.01,
                  override=[
                      dict(name="conv_reg"),
                      dict(name="conv_centerness"),
                      dict(
                          name="conv_cls",
                          type="Normal",
                          std=0.01,
                          bias_prob=0.01,
                      ),
                  ],
              ),
          ]
      super().__init__(*args, init_cfg=init_cfg, **kwargs)
  
file_path: mmdetection/mmdet/models/dense_heads/nasfcos_head.py
imports: import copy
  import torch.nn
  from mmcv.cnn import ConvModule, Scale
  from mmdet.models.dense_heads.fcos_head import FCOSHead
  from mmdet.registry import MODELS
  from mmdet.utils import OptMultiConfig
label_desc: Use image node_id 1 to create a new NASFCOSHead object from inherited base classes: FCOSHead with example: obj = NASFCOSHead()